Analyse des données RNAseq DepMap (CCLE ; cancer) et des 57 epigenomes de Roadmap
# W O R K I N G D I R E C T O R Y
#main.dir = "/shared/projects/chrom_enhancer_bc"
main.dir = file.path("/home/antonin/Documents/stage_2022/git/chrom_enhancer_bc/")
setwd(main.dir)
#data.dir = file.path(main.dir,"data/expression/rdata")
data.dir = "/home/antonin/Documents/stage_2022/data/expression/rdata/"
# I M P O R T
library(pheatmap)
library(ggpubr)
library(sva)
library(edgeR)
library(DESeq2)
library(DT)
library(stringr)
library(dplyr)
library(reshape2)
library(FactoMineR)
library(factoextra)
library(grid)
library(cowplot)
source("./scripts/Normalization.R")
load(file.path(data.dir,"cancer_healthy_expression.RData"))
Id_to_genes = data.frame(expr$gene_name, expr$ENSEMBL_id)
of_interest = c(
"POU5F1" ="ENSG00000204531",
"MYC" = "ENSG00000136997",
"POLR3GL"="ENSG00000121851",
"POLR3G" ="ENSG00000113356",
"MEF2C" = "ENSG00000081189",
"CETN3" = "ENSG00000153140",
"MBLAC2" ="ENSG00000176055",
"LYSMD3" ="ENSG00000176018",
"ADGRV1" ="ENSG00000164199")
rows_of_interest = expr[expr$gene_name %in% names(of_interest) ,]
rows_of_interest$gene_name = NULL
row.names(rows_of_interest) = rows_of_interest$ENSEMBL_id
rows_of_interest$ENSEMBL_id = NULL
# F U N C T I O N S
# Centering by the median
MedianCentering <- function(x){
(x - median(x))
}
get_color_scale_pheatmap = function(matrix,ncolors = 50,threshold = 1){
range <- max(abs(matrix))
myBreaks = seq(-range, range, length.out = ncolors)
myBreaks[myBreaks > -threshold & myBreaks < threshold ] = 0
myBreaks = unique(myBreaks)
paletteLength <- length(myBreaks)
myColor <- colorRampPalette(c("blue", "white", "red"))(paletteLength-1)
return(list("colorscale" = myColor, "breaks" = myBreaks)) }
# D A T A P R O C E S S
# change format
cell_lines = names(expr)[! names(expr) %in% c("gene_name","ENSEMBL_id")]
design = c(rep("CCLE", 20 ), rep("RoadMap",length(cell_lines)-20))
expr = select(expr, -gene_name)
# as numeric
expr <- cbind("ENSEMBL_id"=expr$ENSEMBL_id,
mutate_all(select(expr,all_of(cell_lines)), function(x) as.numeric(as.character(x))))
# Remove lowly expressed genes + 1 duplicated id
expr <- expr %>%
filter(! duplicated(ENSEMBL_id))
row.names(expr) = expr$ENSEMBL_id
expr$ENSEMBL_id = NULL
# Remove lowly expressed track
#Row_sums = rowSums(expr)
#filtered_expr = expr[Row_sums > 3*ncol(expr),]
# Remove track that contain a sample that does not express it
#expr[expr == 0] = NA
#filtered_expr = expr %>% filter(rowMeans(is.na(.)) < 0.9)
#filtered_expr[is.na(filtered_expr)] = 0
# Replace 0 by NA (simplify computation step)
expr[expr == 0] = NA
# differentiate CCLE / RoadMap
CCLE_dataset = expr[, 1:20]
RoadMap_dataset = expr[, 21:length(design)]
# Filtering rows having more than 50% of 0 / NA
#CCLE_dataset$ROWSUM = rowSums(CCLE_dataset)
non_null_CCLE = CCLE_dataset %>%
#filter(ROWSUM > 3*ncol(CCLE_dataset)) %>%
filter(rowMeans(is.na(.)) < 0.25)
#select(-ROWSUM)
#RoadMap_dataset$ROWSUM = rowSums(RoadMap_dataset)
non_null_RoadMap = RoadMap_dataset %>%
#filter(ROWSUM > 3*ncol(RoadMap_dataset)) %>%
filter(rowMeans(is.na(.)) < 0.25)
#select(-ROWSUM)
# Retriving rows that pass filters
to_keep = intersect(row.names(non_null_CCLE) , row.names(non_null_RoadMap))
expr = expr[to_keep, ]
expr[is.na(expr)] = 0
# Using the edgeR filterByExpr function
design = c(rep("CCLE", 20 ), rep("RoadMap",length(cell_lines)-20))
edgeR = DGEList(counts = expr, group = factor(design))
keep = filterByExpr(edgeR, group = design)
rows_to_keep = row.names(edgeR[keep,]$counts)
rows_to_keep = unique(c(rows_to_keep, row.names(rows_of_interest)))
filtered_expr = expr[rows_to_keep, ]
filtered_expr[is.na(filtered_expr)] = 0
# Add the rows of interest (if they were removed)
#filtered_expr = rbind(filtered_expr, rows_of_interest)
#filtered_expr = distinct(filtered_expr)
##### Ajouter les gènes intéressants dans la sélection
##### refaire le calcul avec combat seq
#filtered_expr = filtered_expr[apply(filtered_expr != 0, 1 , all),]
DT::datatable(head(filtered_expr))
# Parameters
design = c(rep("CCLE", 20 ), rep("RoadMap",length(cell_lines)-20))
count.matrix = data.matrix(filtered_expr)
All_normalisations = function(count.matrix,design){
norm_list = list()
tools=c("TMM", "TMMwsp", "RLE", "upperquartile")
# edgeR
for (tool in tools){
message(tool)
edgeR.dgelist = DGEList(counts = count.matrix, group = factor(design))
nf = calcNormFactors(edgeR.dgelist, method=tool)
edgeR_norm <- cpm(nf, normalized.lib.sizes=TRUE, log = TRUE)
norm_list[[tool]] = edgeR_norm
}
#DESeq2
message("DESeq2")
deseq_norm = tools.norm.RNAseq(count.matrix, tool = "vst2", design = design )
norm_list[["DESeq2"]] = deseq_norm
return(norm_list)
}
norm_list = All_normalisations(count.matrix,design)
## TMM
## TMMwsp
## RLE
## upperquartile
## DESeq2
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## Applying limma batch correction
norm_list_limma = lapply(norm_list, function(x) removeBatchEffect(x, design) )
norm_list_limma_melted = lapply(norm_list_limma, function(x) reshape2::melt(x) )
# ComBat batch corrections : needs raw counts
#load(file.path(data.dir,"ComBat_seq_correction.RData"))
adjusted = ComBat_seq(count.matrix, batch=design, group=NULL)
## Found 2 batches
## Using null model in ComBat-seq.
## Adjusting for 0 covariate(s) or covariate level(s)
## Estimating dispersions
## Fitting the GLM model
## Shrinkage off - using GLM estimates for parameters
## Adjusting the data
norm_list_ComBat = All_normalisations(adjusted,design)
## TMM
## TMMwsp
## RLE
## upperquartile
## DESeq2
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
norm_list_ComBat_melted = lapply(norm_list_ComBat, function(x) reshape2::melt(x) )
Scatterplot Limma batch correction vs ComBat_seq
k562 = colnames(norm_list_limma[['TMM']])[grepl("K562",colnames(norm_list_limma[['TMM']]))]
a549 = colnames(norm_list_limma[['TMM']])[grepl("A549",colnames(norm_list_limma[['TMM']]))]
common_cells= c(k562, a549)
Limma_k562_a549 = lapply(norm_list_limma, function(x) select(data.frame(x), all_of(common_cells) ))
ComBat_k562_a549 = lapply(norm_list_ComBat, function(x) select(data.frame(x), all_of(common_cells) ))
polr3g ="ENSG00000113356"
polr3g_full_name = rownames(count.matrix)[grepl(pattern = polr3g, rownames(count.matrix))]
tmp = ComBat_k562_a549$TMM
strange_genes = row.names(filter(tmp, (A549_LUNG < -4.5 & A549 > 2)))
conversion = Id_to_genes[Id_to_genes$expr.ENSEMBL_id %in% strange_genes, ]
print(conversion)
## expr.gene_name expr.ENSEMBL_id
## 901 ARHGEF18 ENSG00000104880.13
## 13519 SCHIP1 ENSG00000151967.14
Exploration = filtered_expr[strange_genes, ]
pheatmap(as.data.frame(t(log2(Exploration+1))))
plot_cell_lines = function(count_matrix, highlight_gene, cell_lines = "K562"){
if (cell_lines == "K562") {
df = select(count_matrix, K562_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE,K562)
plot = ggplot(df, aes(x = K562_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE, y= K562 )) + geom_point()
max_y = max(df[["K562"]])
}
else if (cell_lines == "A549"){
df = select(count_matrix, A549_LUNG,A549)
plot = ggplot(df, aes(x = A549_LUNG, y= A549 )) + geom_point()
max_y = max(df[["A549"]])
}
plot2 = plot +
geom_point(data = df[highlight_gene,],
color = "red",
size = 3)+
geom_point(data = df[strange_genes, ],
color = "green", size = 3) +
geom_smooth(method = "lm") +
stat_regline_equation(label.y = 1.1*max_y, aes(label = ..eq.label..)) +
stat_regline_equation(label.y = max_y, aes(label = ..rr.label..))
return(plot2) }
Multi_plot_A549_K562 = function(count_matrix, highlight_gene, title){
k562_plot = plot_cell_lines(count_matrix,
highlight_gene = highlight_gene,
cell_lines = "K562") +
ggtitle(paste(title,"K562"))
a549_plot = plot_cell_lines(count_matrix,
highlight_gene = highlight_gene,
cell_lines = "A549") +
ggtitle(paste(title,"A549"))
return( plot_grid(k562_plot, a549_plot, labels = "AUTO") )
}
Multi_plot_A549_K562(data.frame(log2(count.matrix+1)), polr3g_full_name, "Raw counts (log2(x)+1)")
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Multi_plot_A549_K562(Limma_k562_a549$TMM, polr3g_full_name, "TMM normalisation - Limma batch correction")
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Multi_plot_A549_K562(Limma_k562_a549$TMMwsp, polr3g_full_name, "TMMwsp normalisation - Limma batch correction")
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Multi_plot_A549_K562(Limma_k562_a549$RLE, polr3g_full_name, "RLE normalisation - Limma batch correction")
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Multi_plot_A549_K562(Limma_k562_a549$upperquartile, polr3g_full_name, "Upperquartile normalisation - Limma batch correction")
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Multi_plot_A549_K562(Limma_k562_a549$DESeq2, polr3g_full_name, "DESeq2 normalisation - Limma batch correction")
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Multi_plot_A549_K562(ComBat_k562_a549$TMM, polr3g_full_name, "TMM normalisation - Combat_seq batch correction")
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Multi_plot_A549_K562(ComBat_k562_a549$TMMwsp, polr3g_full_name, "TMMwsp normalisation - Combat_seq batch correction")
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Multi_plot_A549_K562(ComBat_k562_a549$RLE, polr3g_full_name, "RLE normalisation - Combat_seq batch correction")
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Multi_plot_A549_K562(ComBat_k562_a549$upperquartile, polr3g_full_name, "Upperquartile normalisation - Combat_seq batch correction")
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Multi_plot_A549_K562(ComBat_k562_a549$DESeq2, polr3g_full_name, "DESeq2 normalisation - Combat_seq batch correction")
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Test des normalisations seules vs log2(raw+1)
ctcfl = "ENSG00000124092"
ctcf = "ENSG00000102974"
pou5f1 ="ENSG00000204531"
myc = "ENSG00000136997"
polr3gl="ENSG00000121851"
polr3g ="ENSG00000113356"
CCLE = cell_lines[1:20]
ROADMAP = cell_lines[21:length(names(expr))]
#melted_expr = reshape2::melt(edgeR_norm)
condition = ifelse(norm_list_ComBat_melted$TMM$Var2 %in% CCLE, yes = "CCLE", no = "RoadMap")
# cancer lines of interest :
of_interest = c("MDAMB231","MDAMB436","HCC1937", "HCC1806",
"HCC1954","MDAMB468","K562")
color_cell = ifelse(grepl(paste(of_interest,collapse = "|"), cell_lines ), yes = "red", no ="black")
expression_plot = function(melted_df){
plot = ggplot(data = melted_df, aes(x=Var2, y=value, color = condition)) +
geom_boxplot() +
geom_text(aes(x=Var2,
label=ifelse(grepl(polr3g,Var1),".",''))
, size= 10, color = "black") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, colour = color_cell),
axis.text=element_text(size=6)) +
xlab("cell_lines") + ylab("normalized_expression")
return(plot)
}
compute_plot = function(liste, title = "Normalisation"){
first_part_title = "Expression boxplot :"
c = 1
plot_list = list()
for (df in liste) {
tmp_title = paste(first_part_title, names(liste)[c] ,title)
plot = expression_plot(df) + ggtitle(tmp_title)
plot_list[[ names(liste)[c] ]] = plot
c = c+1
}
return(plot_list) }
# Raw + log2
melt_raw = reshape2::melt(log2(count.matrix+1))
raw_log = expression_plot(melt_raw) + ggtitle("Expression boxplot : Raw expression (log2+1)")
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
norm_list_melted = lapply(norm_list, function(x) reshape2::melt(x) )
norm_plot = compute_plot(norm_list_melted)
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
## Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
## Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
## Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
## Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
print(norm_plot$DESeq2)
for (plot in norm_plot){
print(plot)
}
Test des batchs correction seules vs DESeq2 norm vs log2(raw+1)
# Raw + log2
melt_raw = reshape2::melt(log2(count.matrix+1))
expression_plot(melt_raw) + ggtitle("Expression boxplot : Raw expression (log2+1)")
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
# Norm + batch(limma)
batch_norm_melt = norm_list_limma_melted$DESeq2
expression_plot(batch_norm_melt) + ggtitle("Expression boxplot : Normalized expression + batch correction (limma)")
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
# Norm + batch(ComBat)
batch_norm_combat_melt = norm_list_ComBat_melted$DESeq2
expression_plot(batch_norm_combat_melt) + ggtitle("Expression boxplot : Normalized expression + batch correction (Combat)")
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
# Raw + batch(ComBat)
batch_raw_combat_melt = reshape2::melt(log2(adjusted+1))
expression_plot(batch_raw_combat_melt) + ggtitle("Expression boxplot : Raw expression (log2+1) + batch correction (Combat)")
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
# raw + batch(limma)
batch_raw = removeBatchEffect(log2(count.matrix+1), design)
batch_raw_melt = reshape2::melt(batch_raw)
expression_plot(batch_raw_melt) + ggtitle("Expression boxplot : Raw expression (log2+1) + batch correction (limma)")
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
Vérification avec PCA
Raw
raw_matrix = data.frame(log2(count.matrix+1))
extract_top_varying_genes = function(matrix, threshold=0.75){
IQRs = data.frame("IQR" = apply(matrix, 1, IQR))
IQRs$IQR = as.numeric(IQRs$IQR)
print(hist(IQRs$IQR))
print("threshold :")
print(unname(quantile(IQRs$IQR,threshold)))
genes = rownames(filter(IQRs, IQRs$IQR> quantile(IQRs$IQR,threshold)))
message("Top ",length(genes)," most varying genes were taken for the PCA")
return(genes )
}
pca_subset = raw_matrix[extract_top_varying_genes(raw_matrix, 0.5) , ]
## $breaks
## [1] 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5
## [16] 8.0 8.5 9.0 9.5 10.0 10.5 11.0
##
## $counts
## [1] 11 1828 4486 1983 1113 876 760 629 544 433 350 285 246 149 107
## [16] 66 41 20 14 4 5
##
## $density
## [1] 0.0015770609 0.2620788530 0.6431541219 0.2843010753 0.1595698925
## [6] 0.1255913978 0.1089605735 0.0901792115 0.0779928315 0.0620788530
## [11] 0.0501792115 0.0408602151 0.0352688172 0.0213620072 0.0153405018
## [16] 0.0094623656 0.0058781362 0.0028673835 0.0020071685 0.0005734767
## [21] 0.0007168459
##
## $mids
## [1] 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25 5.75 6.25
## [13] 6.75 7.25 7.75 8.25 8.75 9.25 9.75 10.25 10.75
##
## $xname
## [1] "IQRs$IQR"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
## [1] "threshold :"
## [1] 2.126918
## Top 6975 most varying genes were taken for the PCA
of_interest = c("MDAMB231","MDAMB436","HCC1937", "HCC1806",
"HCC1954","MDAMB468")
design_PCA = ifelse(grepl("K562", cell_lines), yes = "K562", no = design)
design_PCA = ifelse(grepl("A549", cell_lines), yes = "A549", no = design_PCA)
#design_PCA = ifelse(grepl(paste(of_interest,collapse = "|"), cell_lines), yes = "TNBC", no = design_PCA)
res.pca = PCA(t(pca_subset), graph = F)
fviz_pca_ind(res.pca,
col.ind = design_PCA,
repel = TRUE,
palette = "Set1",
ggrepel.max.overlaps = 1) + labs(title ="PCA raw matrix",)
Normalisation
norm_pca = data.frame(t(norm_list$DESeq2))
norm_pca = norm_pca[,extract_top_varying_genes(norm_list$DESeq2)]
## $breaks
## [1] 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
##
## $counts
## [1] 174 4141 3825 1882 1308 869 678 423 294 167 82 65 29 9 2
## [16] 2
##
## $density
## [1] 0.0249462366 0.5936917563 0.5483870968 0.2698207885 0.1875268817
## [6] 0.1245878136 0.0972043011 0.0606451613 0.0421505376 0.0239426523
## [11] 0.0117562724 0.0093189964 0.0041577061 0.0012903226 0.0002867384
## [16] 0.0002867384
##
## $mids
## [1] 0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25 5.75 6.25 6.75 7.25
## [16] 7.75
##
## $xname
## [1] "IQRs$IQR"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
## [1] "threshold :"
## [1] 2.154958
## Top 3488 most varying genes were taken for the PCA
res.pca = PCA(norm_pca, graph = F)
fviz_pca_ind(res.pca,
col.ind = design_PCA,
repel = TRUE,
palette = "Set1",
ggrepel.max.overlaps = 1) + labs(title ="PCA normalisad count (DESeq2)",)
Normalisation + Batch correction (ComBat_seq)
norm_pca_combat = data.frame(t(norm_list_ComBat$DESeq2))
norm_pca_combat = norm_pca_combat[,extract_top_varying_genes(norm_list_ComBat$DESeq2,0.75)]
## $breaks
## [1] 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0
##
## $counts
## [1] 377 5549 3411 1693 1199 746 452 273 134 58 30 16 8 4
##
## $density
## [1] 0.0540501792 0.7955555556 0.4890322581 0.2427240143 0.1718996416
## [6] 0.1069534050 0.0648028674 0.0391397849 0.0192114695 0.0083154122
## [11] 0.0043010753 0.0022939068 0.0011469534 0.0005734767
##
## $mids
## [1] 0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25 5.75 6.25 6.75
##
## $xname
## [1] "IQRs$IQR"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
## [1] "threshold :"
## [1] 1.805215
## Top 3488 most varying genes were taken for the PCA
res.pca = PCA(norm_pca_combat, graph = F)
fviz_pca_ind(res.pca,
col.ind = design_PCA,
repel = TRUE,
palette = "Set1",
ggrepel.max.overlaps = 1) + labs(title ="PCA normalisad count (DESeq2) + ComBat_seq",)
Normalisation + Batch correction (limma)
#norm_pca_limma = data.frame(t(norm_list_limma$DESeq2[rownames(filter(IQRs, IQRs$IQR>7)) , ]))
norm_pca_limma = data.frame(t(norm_list_limma$DESeq2))
norm_pca_limma = norm_pca_limma[, extract_top_varying_genes(norm_list_limma$DESeq2,0.7) ]
## $breaks
## [1] 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5
##
## $counts
## [1] 207 5046 3582 1808 1237 862 530 342 177 78 42 19 9 7 4
##
## $density
## [1] 0.0296774194 0.7234408602 0.5135483871 0.2592114695 0.1773476703
## [6] 0.1235842294 0.0759856631 0.0490322581 0.0253763441 0.0111827957
## [11] 0.0060215054 0.0027240143 0.0012903226 0.0010035842 0.0005734767
##
## $mids
## [1] 0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25 5.75 6.25 6.75 7.25
##
## $xname
## [1] "IQRs$IQR"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
## [1] "threshold :"
## [1] 1.728779
## Top 4185 most varying genes were taken for the PCA
res.pca = PCA(norm_pca_limma, graph = F)
fviz_pca_ind(res.pca,
col.ind = design_PCA,
repel = TRUE,
palette = "Set1",
ggrepel.max.overlaps = 1) + labs(title ="PCA normalisad count (DESeq2) + limma ",)
Heatmap d’expression de la région autour de POLR3G
ComBat_norm = norm_list_ComBat_melted$DESeq2
# VARIABLES
TNBC = c("MDAMB231","MDAMB436","HCC1937", "HCC1806",
"HCC1954","MDAMB468")
of_interest = c(
"POU5F1" ="ENSG00000204531",
"MYC" = "ENSG00000136997",
"POLR3GL"="ENSG00000121851",
"POLR3G" ="ENSG00000113356",
"MEF2C" = "ENSG00000081189",
"CETN3" = "ENSG00000153140",
"MBLAC2" ="ENSG00000176055",
"LYSMD3" ="ENSG00000176018",
"ADGRV1" ="ENSG00000164199")
of_interest = data_frame("ENS_id" = of_interest, "gene_name" = names(of_interest))
## Warning: `data_frame()` was deprecated in tibble 1.1.0.
## Please use `tibble()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
# Filtering matrix with only gene of interests
tmp = filter(ComBat_norm, grepl(paste(of_interest$ENS_id,collapse="|"),ComBat_norm$Var1))
tmp$copie = substr(tmp$Var1,1,15)
# merge dataframes to correctly match ENS_ID with gene name
to_heatmap = select(merge(tmp,of_interest, by.x = "copie", by.y = "ENS_id"),
gene_name,Var2 ,value)
colnames(to_heatmap) = c("gene_name","cell_line","expression")
# melt ==> classical matrix
heat = reshape2::dcast(to_heatmap, gene_name~cell_line)
## Using expression as value column: use value.var to override.
row.names(heat) = heat$gene_name
heat$gene_name = NULL
# Median centering
heat_norm = t(apply(heat, 1, MedianCentering))
# Create color scale
scale = get_color_scale_pheatmap(heat_norm,ncolors = 50,threshold = 0.75)
annotation_heatmap = data.frame("data_source" = design, row.names = cell_lines)
color = c("red","blue")
names(color) = c("CCLE","RoadMap")
annotation_heatmap$state = ifelse(
grepl(paste(TNBC,collapse="|") ,row.names(annotation_heatmap)),
yes = "TNBC", no = "other")
ComBat_heatmap = data.frame(t(heat_norm))
ComBat_heatmap$colors = ifelse(grepl(paste(TNBC,collapse="|") ,row.names(ComBat_heatmap)),
yes = "red", no = "darkgrey")
ComBat_heatmap = ComBat_heatmap[order(-ComBat_heatmap$POLR3G) , ]
ComBat_heatmap = ComBat_heatmap[,c("CETN3","MBLAC2","LYSMD3","MYC","POLR3GL","ADGRV1","MEF2C","POLR3G","colors")]
map = pheatmap(select(ComBat_heatmap,-colors),
color = scale$colorscale,
breaks = scale$breaks,
cluster_rows = F, cluster_cols = F,
angle_col = 0,
fontsize_row = 5,
main = "Expression heatmap : ComBat batch correction ")
#tmp = ComBat_heatmap
map$gtable$grobs[[4]]$gp=gpar(col= ComBat_heatmap$colors, fontsize = 5)
print(map)
limma_norm = norm_list_limma_melted$DESeq2
# VARIABLES
TNBC = c("MDAMB231","MDAMB436","HCC1937", "HCC1806",
"HCC1954","MDAMB468")
of_interest = c(
"POU5F1" ="ENSG00000204531",
"MYC" = "ENSG00000136997",
"POLR3GL"="ENSG00000121851",
"POLR3G" ="ENSG00000113356",
"MEF2C" = "ENSG00000081189",
"CETN3" = "ENSG00000153140",
"MBLAC2" ="ENSG00000176055",
"LYSMD3" ="ENSG00000176018",
"ADGRV1" ="ENSG00000164199")
of_interest = data_frame("ENS_id" = of_interest, "gene_name" = names(of_interest))
# Filtering matrix with only gene of interests
tmp = filter(limma_norm, grepl(paste(of_interest$ENS_id,collapse="|"),limma_norm$Var1))
tmp$copie = substr(tmp$Var1,1,15)
tmp = filter(limma_norm, grepl(paste(of_interest$ENS_id,collapse="|"),limma_norm$Var1))
tmp$copie = substr(tmp$Var1,1,15)
to_heatmap = select(merge(tmp,of_interest, by.x = "copie", by.y = "ENS_id"),
gene_name,Var2 ,value)
colnames(to_heatmap) = c("gene_name","cell_line","expression")
heat = reshape2::dcast(to_heatmap, gene_name~cell_line)
## Using expression as value column: use value.var to override.
row.names(heat) = heat$gene_name
heat$gene_name = NULL
heat_norm = t(apply(heat, 1, MedianCentering))
scale = get_color_scale_pheatmap(heat_norm,ncolors = 50,threshold = 0.75)
annotation_heatmap = data.frame("data_source" = design, row.names = cell_lines)
color = c("red","blue")
names(color) = c("CCLE","RoadMap")
annotation_heatmap$state = ifelse(
grepl(paste(TNBC,collapse="|") ,row.names(annotation_heatmap)),
yes = "TNBC", no = "other")
limma_heatmap = data.frame(t(heat_norm))
limma_heatmap$colors = ifelse(grepl(paste(TNBC,collapse="|") ,row.names(limma_heatmap)),
yes = "red", no = "darkgrey")
limma_heatmap = limma_heatmap[order(-limma_heatmap$POLR3G) , ]
limma_heatmap = limma_heatmap[,c("CETN3","MBLAC2","LYSMD3","MYC","POLR3GL","ADGRV1","MEF2C","POLR3G","colors")]
map = pheatmap(select(limma_heatmap,-colors),
color = scale$colorscale,
breaks = scale$breaks,
cluster_rows = F, cluster_cols = F,
angle_col = 0,
fontsize_row = 5,
main = "Expression heatmap : limma batch correction ")
#tmp = ComBat_heatmap
map$gtable$grobs[[4]]$gp=gpar(col= limma_heatmap$colors, fontsize = 5)
print(map)
library(GGally)
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
ComBat_norm = data.frame(norm_list_ComBat$DESeq2)
tmp = filter(ComBat_norm, grepl(paste(of_interest$ENS_id,collapse="|"),substr(row.names(ComBat_norm),1,15) ))
tmp$ENS_id = substr(rownames(tmp),1,15)
tmp = merge(tmp, of_interest, by = "ENS_id")
row.names(tmp) = tmp$gene_name
to_be_plotted = select(tmp, -gene_name, -ENS_id)
design = c(rep("CCLE", 20 ), rep("RoadMap",length(cell_lines)-20))
#to_be_plotted = rbind(to_be_plotted, "condition" = design)
#to_be_plotted
ggpairs(as.data.frame(t(to_be_plotted)),
#aes(colors=design)
mapping = ggplot2::aes(color =design))+
theme_minimal()
library(GGally)
limma_norm = data.frame(norm_list_limma$DESeq2)
tmp = filter(limma_norm, grepl(paste(of_interest$ENS_id,collapse="|"),substr(row.names(limma_norm),1,15) ))
tmp$ENS_id = substr(rownames(tmp),1,15)
tmp = merge(tmp, of_interest, by = "ENS_id")
row.names(tmp) = tmp$gene_name
to_be_plotted = select(tmp, -gene_name, -ENS_id)
design = c(rep("CCLE", 20 ), rep("RoadMap",length(cell_lines)-20))
#to_be_plotted
ggpairs(as.data.frame(t(to_be_plotted)),
#aes(colors=design)
mapping = ggplot2::aes(color =design))+
theme_minimal()
to_be_plotted2 = rbind(to_be_plotted, "condition" = design)
ggparcoord(as.data.frame(t(to_be_plotted2)),
column = 1:9,
groupColumn = "condition",
order = "skewness",
showPoints = TRUE,
scale = "centerObs") +
theme_minimal() +
facet_wrap(~ condition)
